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Surprisal-based comparison between a symbolic and a connectionist model of sentence processing

In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1139--1144 (2009)

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  1. Composition in Distributional Models of Semantics.Jeff Mitchell & Mirella Lapata - 2010 - Cognitive Science 34 (8):1388-1429.
    Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in (...)
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  • A Framework for Modeling the Interaction of Syntactic Processing and Eye Movement Control.Felix Engelmann, Shravan Vasishth, Ralf Engbert & Reinhold Kliegl - 2013 - Topics in Cognitive Science 5 (3):452-474.
    We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and (...)
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  • Probabilistic Modeling of Discourse‐Aware Sentence Processing.Amit Dubey, Frank Keller & Patrick Sturt - 2013 - Topics in Cognitive Science 5 (3):425-451.
    Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more (...)
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  • Knowledge and Implicature: Modeling Language Understanding as Social Cognition.Noah D. Goodman & Andreas Stuhlmüller - 2013 - Topics in Cognitive Science 5 (1):173-184.
    Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the rational speech-act theory: Listeners assume that speakers choose their utterances approximately optimally, and listeners interpret an utterance by using Bayesian inference to “invert” this model of the speaker. We apply this framework to model scalar implicature (“some” implies “not all,” and “N” implies “not more than N”). This model predicts an interaction between the speaker's knowledge state and the listener's interpretation. (...)
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  • Rare Constructions Are More Often Sentence‐Initial.David Temperley - 2019 - Cognitive Science 43 (2):e12714.
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  • Probability and Surprisal in Auditory Comprehension of Morphologically Complex Words.Laura Winther Balling & R. Harald Baayen - 2012 - Cognition 125 (1):80-106.
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